Medical School, Sao Paulo State University Julio de Mesquita Filho, Botucatu, Brazil.
Institute of Bioscience, Sao Paulo State University Julio de Mesquita Filho, Botucatu, Brazil.
Phys Eng Sci Med. 2021 Jun;44(2):387-394. doi: 10.1007/s13246-021-00988-2. Epub 2021 Mar 17.
Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.
评估纹理分析与机器学习方法相结合是否可以区分良恶性淋巴结。共选择了 18 例肺癌患者，共 39 个淋巴结，其中 15 个为恶性，24 个为良性。使用了有和没有对比剂的回顾性计算机断层扫描。这项工作的最大不同之处在于使用了来自纵隔淋巴结的 15 种纹理，有 5 位不同的医生作为操作者。提取了一阶和二阶统计纹理，如灰度游程长度和共生矩阵，并应用于三种不同的机器学习分类器。最佳的机器学习分类器在操作者之间的变异性小于 5%。支持向量机 (SVM) 分类器在无对比剂序列中呈现出 95%的ROC 曲线下面积（AUC）和 89%的敏感性。SVM 分类器在有对比剂的序列中呈现出 93%的 AUC 和 86%的敏感性。纹理分析和机器学习可能有助于区分良恶性淋巴结。这项研究可以帮助医生对淋巴结进行诊断和分期，并有可能减少对组织病理学确认的侵入性分析数量。
